Scalable Machine Learning Analysis of Parker Solar Probe Solar Wind Data

TL;DR

This paper introduces a scalable machine learning framework utilizing distributed processing and quantum-inspired methods to analyze large-scale Parker Solar Probe solar wind data, revealing key trends and providing tools for future space weather research.

Contribution

The paper presents a novel scalable analysis framework combining Dask and Kernel Density Matrices for large solar wind datasets, enabling detailed distribution estimation and anomaly detection.

Findings

Solar wind speed increases with distance from the Sun.

Proton density decreases with distance from the Sun.

Inverse relationship between solar wind speed and proton density.

Abstract

We present a scalable machine learning framework for analyzing Parker Solar Probe (PSP) solar wind data using distributed processing and the quantum-inspired Kernel Density Matrices (KDM) method. The PSP dataset (2018--2024) exceeds 150 GB, challenging conventional analysis approaches. Our framework leverages Dask for large-scale statistical computations and KDM to estimate univariate and bivariate distributions of key solar wind parameters, including solar wind speed, proton density, and proton thermal speed, as well as anomaly thresholds for each parameter. We reveal characteristic trends in the inner heliosphere, including increasing solar wind speed with distance from the Sun, decreasing proton density, and the inverse relationship between speed and density. Solar wind structures play a critical role in enhancing and mediating extreme space weather phenomena and can trigger geomagnetic storms; our analyses provide quantitative insights into these processes. This approach offers a tractable, interpretable, and distributed methodology for exploring complex physical datasets and facilitates reproducible analysis of large-scale in situ measurements. Processed data products and analysis tools are made publicly available to advance future studies of solar wind dynamics and space weather forecasting. The code and configuration files used in this study are publicly available to support reproducibility.